Agentic education · Learning science

Actionable companion · Retention & transfer

Learning with agency: Perceptions, Usage, and Educational Impact of ChatGPT Among Medical Students in Germany: Cross-Sectional Mixed Methods Survey

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Large language models such as ChatGPT offer significant opportunities for medical education. However, empirical data on actual usage patterns, perceived benefits, and limitations among medical students remain limited. Objective This study aimed to assess how medical students in Germany use ChatGPT, their perceptions of its educational value, and the challenges and concerns associated with its use. Methods A cross-sectional 17-item online survey was conducted between May and August 2024 among medical students from Philipps University Marburg, Germany. A mixed methods approach was applied, combining descriptive and inferential statistical analysis with qualitative content analysis of open-ended responses. Results A total of 84 fully completed surveys were included in the analysis (response rate: 26.7%; 315 surveys started). Overall, 76.2% (64/84) of the participants reported having used ChatGPT for medical education, with significantly higher usage during exam periods (P=.003). Preclinical students reported higher overall usage than clinical students (P=.02). ChatGPT was primarily used for summarizing information by 60.7% (51/84) of students, for literature research by 57.7% (49/84), and for clarifying concepts by 47.1% (40/84). A total of 70.2% (59/84) felt that it helped them save time, and 51.2% (43/84) reported an improved understanding of content. In contrast, only 31% (26/84) saw benefits for applying knowledge and 15.5% (13/84) for long-term knowledge retention. Qualitative responses highlighted clear benefits such as time savings and support in exam preparation, while also pointing to potential applications in clinical documentation and expressing concerns about misinformation and source transparency. However, 73.3% (55/75) expressed concerns about misinformation, and 72.6% (61/84) reported lacking confidence in their artificial intelligence (AI)–related skills. Only 41.7% (35/84) stated that they trust ChatGPT’s outputs. Students who used the tool more frequently also reported higher levels of trust in ChatGPT’s outputs (r=0.374, P<.001). Over 70% of respondents indicated a strong desire for increased integration of AI-related education and practical applications within the medical curriculum. Conclusions ChatGPT was already widely used among medical students, especially in exam preparation and the early stages of training. Students valued its efficiency and support for understanding complex material, but its long-term influence on learning is limited. Concerns about reliability, source transparency, and data privacy remain, and AI skills played a key role in shaping usage. These findings underscore the need to integrate structured, practice-oriented AI education into medical training to support critical, informed, and ethical use of large language models.

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The survey encompassed a total of 17 questions (Multimedia Appendix 1) and was reported in accordance with the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) checklist [15]. The web-based survey was conducted via SoSciSurvey between May and August 2024. Medical students from University Marburg were recruited through student mailing lists, medical lectures at Philipps University Marburg, and by personal invitation from AF. The open survey was developed based on a review of related literature [16-18]. Adaptive questioning was applied to tailor the survey experience and reduce participant burden. Participants who indicated using other educational software or AI tools were asked to specify which ones. The following open-ended questions were included: “What concerns do you have when using ChatGPT?” “In which areas do you see potential in using ChatGPT?” “For which subjects or areas do you primarily use ChatGPT and why?” The survey was pretested for usability and technical functionality by 3 medical students who were not part of the final sample. Their feedback was used to refine the wording, layout, and navigation of the questionnaire before launching the final version via SoSciSurvey.

Limitations and uncertainty · source passage

This study is subject to several limitations. Our findings should be viewed as a snapshot of early ChatGPT adoption, which may not fully capture current practices due to rapid AI developments. Furthermore, data collection was based on a voluntary online questionnaire. This may have introduced a self-selection bias, as students with an interest in technology may have been more likely to participate. Additionally, reliance on self-reported measures may introduce inaccuracies due to factors such as social desirability and recall bias. As a result, the reported ChatGPT usage in this study could be overestimated compared to participants’ actual average usage. Although 315 students accessed the survey, only 84 provided complete responses, which reflects the generally low response rates commonly observed in medical education online surveys [33]. This relatively low completion rate may be attributed to several factors, including the voluntary nature of participation, the overall length of the questionnaire, and the inclusion of open-ended questions, which may have required more time and cognitive effort. A further limitation is that the analysis of open-ended responses was conducted by a single researcher, which may have introduced subjectivity and limited intercoder reliability. Although this approach ensured consistency in coding, future studies would benefit from involving multiple coders and formal reliability checks to strengthen the robustness of qualitative findings.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Anna Fußhöller; Fabian Lechner; Nadine Schlicker; Felix Muehlensiepen; Andreas Mayr; Sebastian Kuhn; Martin Christian Hirsch; Johannes Knitza. Perceptions, Usage, and Educational Impact of ChatGPT Among Medical Students in Germany: Cross-Sectional Mixed Methods Survey. JMIR Formative Research (2025-11-11). https://doi.org/10.2196/81484

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.